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# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you under
# the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
# Vector database example
Requirements:
$ pip install nltk sentence_transformers tqdm "elasticsearch"
To run the example:
$ python vectors.py "text to search"
The index will be created automatically if it does not exist. Add
`--recreate-index` to regenerate it.
The example dataset includes a selection of workplace documents. The
following are good example queries to try out with this dataset:
$ python vectors.py "work from home"
$ python vectors.py "vacation time"
$ python vectors.py "can I bring a bird to work?"
When the index is created, the documents are split into short passages, and for
each passage an embedding is generated using the open source
"all-MiniLM-L6-v2" model. The documents that are returned as search results are
those that have the highest scored passages. Add `--show-inner-hits` to the
command to see individual passage results as well.
"""
import argparse
import json
import os
from datetime import datetime
from typing import Any, List, Optional, cast
from urllib.request import urlopen
import nltk
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
from elasticsearch.dsl import (
DenseVector,
Document,
InnerDoc,
Keyword,
M,
Search,
connections,
mapped_field,
)
DATASET_URL = "https://raw.githubusercontent.com/elastic/elasticsearch-labs/main/datasets/workplace-documents.json"
MODEL_NAME = "all-MiniLM-L6-v2"
# initialize sentence tokenizer
nltk.download("punkt_tab", quiet=True)
# this will be the embedding model
embedding_model: Any = None
class Passage(InnerDoc):
content: str
embedding: List[float] = mapped_field(DenseVector())
class WorkplaceDoc(Document):
class Index:
name = "workplace_documents"
name: str
summary: str
content: str
created: datetime
updated: Optional[datetime]
url: str = mapped_field(Keyword(required=True))
category: str = mapped_field(Keyword(required=True))
passages: M[List[Passage]] = mapped_field(default=[])
@classmethod
def get_embedding(cls, input: str) -> List[float]:
global embedding_model
if embedding_model is None:
embedding_model = SentenceTransformer(MODEL_NAME)
return cast(List[float], list(embedding_model.encode(input)))
def clean(self) -> None:
# split the content into sentences
passages = cast(List[str], nltk.sent_tokenize(self.content))
# generate an embedding for each passage and save it as a nested document
for passage in passages:
self.passages.append(
Passage(content=passage, embedding=self.get_embedding(passage))
)
def create() -> None:
# create the index
WorkplaceDoc._index.delete(ignore_unavailable=True)
WorkplaceDoc.init()
# download the data
dataset = json.loads(urlopen(DATASET_URL).read())
# import the dataset
for data in tqdm(dataset, desc="Indexing documents..."):
doc = WorkplaceDoc(
name=data["name"],
summary=data["summary"],
content=data["content"],
created=data.get("created_on"),
updated=data.get("updated_at"),
url=data["url"],
category=data["category"],
)
doc.save()
def search(query: str) -> Search[WorkplaceDoc]:
return WorkplaceDoc.search().knn(
field=WorkplaceDoc.passages.embedding,
k=5,
num_candidates=50,
query_vector=list(WorkplaceDoc.get_embedding(query)),
inner_hits={"size": 2},
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Vector database with Elasticsearch")
parser.add_argument(
"--recreate-index", action="store_true", help="Recreate and populate the index"
)
parser.add_argument(
"--show-inner-hits",
action="store_true",
help="Show results for individual passages",
)
parser.add_argument("query", action="store", help="The search query")
return parser.parse_args()
def main() -> None:
args = parse_args()
# initiate the default connection to elasticsearch
connections.create_connection(hosts=[os.environ["ELASTICSEARCH_URL"]])
if args.recreate_index or not WorkplaceDoc._index.exists():
create()
results = search(args.query)
for hit in results:
print(
f"Document: {hit.name} [Category: {hit.category}] [Score: {hit.meta.score}]"
)
print(f"Summary: {hit.summary}")
if args.show_inner_hits:
for passage in hit.meta.inner_hits["passages"]:
print(f" - [Score: {passage.meta.score}] {passage.content!r}")
print("")
# close the connection
connections.get_connection().close()
if __name__ == "__main__":
main()
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